TEMPLATE BULLSHIT
TO EDIT
ADD DATA SOURCES!!!!!!!!!!!!!!!
| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Mazda RX4 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
| Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
| Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
| Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
| Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
| Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |
| Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0 | 0 | 3 | 4 |
| Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.190 | 20.00 | 1 | 0 | 4 | 2 |
| Merc 230 | 22.8 | 4 | 140.8 | 95 | 3.92 | 3.150 | 22.90 | 1 | 0 | 4 | 2 |
| Merc 280 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.30 | 1 | 0 | 4 | 4 |
| Merc 280C | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.90 | 1 | 0 | 4 | 4 |
| Merc 450SE | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.070 | 17.40 | 0 | 0 | 3 | 3 |
| Merc 450SL | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.730 | 17.60 | 0 | 0 | 3 | 3 |
| Merc 450SLC | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.780 | 18.00 | 0 | 0 | 3 | 3 |
| Cadillac Fleetwood | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.250 | 17.98 | 0 | 0 | 3 | 4 |
| Lincoln Continental | 10.4 | 8 | 460.0 | 215 | 3.00 | 5.424 | 17.82 | 0 | 0 | 3 | 4 |
| Chrysler Imperial | 14.7 | 8 | 440.0 | 230 | 3.23 | 5.345 | 17.42 | 0 | 0 | 3 | 4 |
| Fiat 128 | 32.4 | 4 | 78.7 | 66 | 4.08 | 2.200 | 19.47 | 1 | 1 | 4 | 1 |
| Honda Civic | 30.4 | 4 | 75.7 | 52 | 4.93 | 1.615 | 18.52 | 1 | 1 | 4 | 2 |
| Toyota Corolla | 33.9 | 4 | 71.1 | 65 | 4.22 | 1.835 | 19.90 | 1 | 1 | 4 | 1 |
| Toyota Corona | 21.5 | 4 | 120.1 | 97 | 3.70 | 2.465 | 20.01 | 1 | 0 | 3 | 1 |
| Dodge Challenger | 15.5 | 8 | 318.0 | 150 | 2.76 | 3.520 | 16.87 | 0 | 0 | 3 | 2 |
| AMC Javelin | 15.2 | 8 | 304.0 | 150 | 3.15 | 3.435 | 17.30 | 0 | 0 | 3 | 2 |
| Camaro Z28 | 13.3 | 8 | 350.0 | 245 | 3.73 | 3.840 | 15.41 | 0 | 0 | 3 | 4 |
| Pontiac Firebird | 19.2 | 8 | 400.0 | 175 | 3.08 | 3.845 | 17.05 | 0 | 0 | 3 | 2 |
| Fiat X1-9 | 27.3 | 4 | 79.0 | 66 | 4.08 | 1.935 | 18.90 | 1 | 1 | 4 | 1 |
| Porsche 914-2 | 26.0 | 4 | 120.3 | 91 | 4.43 | 2.140 | 16.70 | 0 | 1 | 5 | 2 |
| Lotus Europa | 30.4 | 4 | 95.1 | 113 | 3.77 | 1.513 | 16.90 | 1 | 1 | 5 | 2 |
| Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.170 | 14.50 | 0 | 1 | 5 | 4 |
| Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.770 | 15.50 | 0 | 1 | 5 | 6 |
| Maserati Bora | 15.0 | 8 | 301.0 | 335 | 3.54 | 3.570 | 14.60 | 0 | 1 | 5 | 8 |
| Volvo 142E | 21.4 | 4 | 121.0 | 109 | 4.11 | 2.780 | 18.60 | 1 | 1 | 4 | 2 |
The gtfsrouter package allows us to calculate all stations reachable within a specified time period from a nominated station (isochrones). We use the hull polygon as an indicator for the city area reachable.
See below for a isochrone plot of a 30min Monday work–home trip starting from Helmholtzstraße at 18:00.
We try to simulate a home–work trip in the morning rush hour arriving at 08:00. Assuming a five minute walk at beginning and end of the trip, we limit the GTFS data to Monday (2021-01-18) and calculate the isochrones for a 40min time range. By that we want to get all reachable stations by local transport (< 60min, PBefG §8 (1)) and rate the station inside the network.
The local transport plan (p. 106) sets targets for the connectivity standards. Different categories of center areas (see StEP, p. 45) have to be reachable within a certain time and with a maximum number of transfer. This should hold for 95% of the stations.
Based on the GTFS data, we try to recreate the result of the monitoring (NVP Anlage 1, p. 12), mentioning a degree of fulfillment of 99,7% for the central areas.
From a more detailed display of the area (p. 39), we create a shape file enclosing the associated stations. The tidytransit package let’s us calculate the shortest travel time for all stations to any of a specified set of stations. For that arrival has to be set to TRUE.
We try to simulate a home–work trip in the morning rush hour arriving at 08:00. Assuming five minute walking at beginning and end of the trip, we limit the GTFS data to Monday (2021-01-18) and calculate the shortest travel times in a 90min time range ending at 07:55.
98.99 %
https://rstudio.github.io/leaflet/
Interactive panning/zooming
Compose maps using arbitrary combinations of map tiles, markers, polygons, lines, popups, and GeoJSON.
Create maps right from the R console or RStudio
Embed maps in knitr/R Markdown documents and Shiny apps
Easily render Spatial objects from the sp package, or data frames with latitude/longitude columns
Use map bounds and mouse events to drive Shiny logic
---
title: "Data Science Transport – Second Assignment – Group 12"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(gtfsrouter)
library(tidyverse)
library(tidytransit)
library(sf)
library(tmap)
library(units)
library(RColorBrewer)
tmap_mode("view")
```
last time {data-icon="fa-hourglass-half"}
=====================================
TEMPLATE BULLSHIT
TO EDIT
ADD DATA SOURCES!!!!!!!!!!!!!!!
- test1
- test2
### Chart 0
```{r}
library(leaflet)
leaflet() %>%
addTiles() %>%
addMarkers(lng=174.768, lat=-36.852, popup="The birthplace of R")
```
### Chart 1
```{r}
# 1. Plot the dots themselves
```
### Cars
```{r}
knitr::kable(mtcars)
```
isochrones {data-icon="fa-expand-arrows-alt"}
=====================================
```{r, include = FALSE}
##############################################################
#
# READ GTFS DATA
#
##############################################################
# set work directions
setwd_gtfs <- function(){setwd("~/Documents/Uni/Master/DataScienceTransport/data/vbb-gtfs")}
setwd_data <- function(){setwd("~/Documents/Uni/Master/DataScienceTransport/data")}
setwd_work <- function(){setwd("~/Documents/Uni/Master/DataScienceTransport/assignment_2")}
setwd_work
# read gtfs data for monday
file <- file.path("~/Documents/Uni/Master/DataScienceTransport/data/vbb-gtfs/2020-12_2020-12-28.zip")
gtfs <- extract_gtfs(file) %>% gtfs_timetable(day = 2)
##############################################################
#
# SET TIMES
#
##############################################################
start_time <- 7 * 3600 + 1200
end_time <- 8 * 3600
# create isochrone
# ic <- gtfs_isochrone (gtfs,
# from = from,
# start_time = start_time,
# end_time = end_time)
##############################################################
#
# CREATE STOPS SF OBJECT
#
##############################################################
stops <- st_as_sf(gtfs$stops,
coords = c("stop_lon", "stop_lat"),
crs = 4326) %>%
st_transform(25833)
##############################################################
#
# SHAPE DISTRICTS NEW (+ area)
#
##############################################################
setwd_data()
shape_districts_new <- read_sf(dsn = "LOR_SHP_2019-1", layer = "Planungsraum_EPSG_25833")
setwd_work()
shape_districts_new <- shape_districts_new %>%
group_by(BEZIRK) %>%
summarise() %>%
filter(!is.na(BEZIRK)) %>%
rename(NAME = BEZIRK) %>%
mutate(AREA = st_area(geometry)) %>%
select(NAME, AREA, everything()) %>%
mutate(AREA = (AREA / 1000000) * as_units("km2"))
# setting crs of polygons
st_crs(shape_districts_new$geometry) <- 25833
shape_berlin <- st_union(shape_districts_new)
##############################################################
#
# SPECIFIC SHAPES AND STOPS
#
##############################################################
stops_in_berlin <- stops %>%
mutate(inside_berlin = st_within( geometry, shape_berlin )) %>%
mutate(inside_berlin = !is.na( as.numeric( inside_berlin ))) %>%
filter(inside_berlin == TRUE)
# get isochrone area
# ic = gtfs_isochrone (gtfs,
# from = "Berlin, Sowjetisches Ehrenmal",
# #from_is_id = TRUE,
# start_time = start_time,
# end_time = end_time)$hull$area
##############################################################
#
# CALCULATE ISOCHRONES
#
##############################################################
# # the following code calculates the isochrones (inefficent, ~ 15h)
# # instead of running the code, we read in the pre-calculated file
# stops_ic_area <- vector(mode = "double")
#
# # create isochrone areas for stops in 50 minutes
# for (stop_name in stops$stop_name){
#
# tryCatch( {
# ic_area <- gtfs_isochrone (gtfs,
# from = stop_name,
# #from_is_id = TRUE,
# start_time = start_time,
# end_time = end_time)$hull$area
# if(is.null(ic_area)) {
# stops_ic_area <<- rbind(stops_ic_area, 0)
# print(paste(stop_name, ": ", ic_area, "!!!!!!!!!!"))
# } else {
# stops_ic_area <<- rbind(stops_ic_area, ic_area)
# print(paste(stop_name, ": ", ic_area))
# }
# },
# error = function(e) {
# stops_ic_area <<- rbind(stops_ic_area, 0)
# print(paste("ERROR!!!", stop_name))
# }
# )
# }
#
# ##############################################################
# #
# # CLEANING
# #
# ##############################################################
#
# # merge and clean
# # https://r-spatial.github.io/sf/reference/bind.html
# # https://cran.r-project.org/web/packages/units/vignettes/units.html
# rownames(stops_ic_area) <- NULL
# stops_area <- st_sf(data.frame(stops, stops_ic_area / 1000000)) %>%
# rename(ic_area = stops_ic_area.1e.06,
# id = stop_id,
# name = stop_name,
# parent = parent_station) %>%
# select(id, name, parent, ic_area) %>%
# mutate(ic_area = ic_area * as_units("km2"))
#
# # save
# # https://r-spatial.github.io/sf/reference/st_write.html
# st_write(stops_area, "output_stops_ic_area.shp")
stops_area <- st_read("output_stops_ic_area.shp")
# more cleaning for plot
# https://dplyr.tidyverse.org/reference/distinct.html
stops_area =
stops_area %>%
select(name, ic_area) %>%
distinct(name, .keep_all = TRUE)
stops_area_berlin <- stops_area %>%
mutate(inside_berlin = st_within( geometry, shape_berlin )) %>%
mutate(inside_berlin = !is.na( as.numeric( inside_berlin ))) %>%
filter(inside_berlin == TRUE) %>%
select(-inside_berlin) %>%
mutate(id = paste(name, ": ", round(ic_area)))
```
Column {data-width=100}
-------------------------------------
### Approach {data-height=200}
The [gtfsrouter](https://atfutures.github.io/gtfs-router/) package allows us to calculate all stations reachable within a specified time period from a nominated station ([isochrones](https://atfutures.github.io/gtfs-router/reference/gtfs_isochrone.html)). We use the hull polygon as an indicator for the city area reachable.
See below for a isochrone plot of a 30min Monday work–home trip starting from Helmholtzstraße at 18:00.
We try to simulate a home–work trip in the morning rush hour arriving at 08:00. Assuming a five minute walk at beginning and end of the trip, we limit the GTFS data to Monday (2021-01-18) and calculate the isochrones for a 40min time range. By that we want to get all reachable stations by local transport (< 60min, [PBefG §8 (1)](https://www.gesetze-im-internet.de/pbefg/__8.html)) and rate the station inside the network.
- reachablity of possible work places
- calculation with all transport agencies
-- domination of the hull area in 40 min time range by reachable fanning out long-distance transport
- no weighting of the reachable area
### 30min IC home trip (18:00) from Helmholtzstr. {data-height=100}
```{r}
ic_einstein <- gtfs_isochrone(gtfs,
from = "Berlin, Helmholtzstr.",
start_time = 18 * 3600,
end_time = 18 * 3600 + 1800)
tm_basemap(leaflet::providers$OpenStreetMap.DE) +
tm_shape(ic_einstein$hull) +
tm_polygons(col = "red",
alpha = 0.2,
border.col = "red") +
tm_shape(ic_einstein$routes) +
tm_lines() +
tm_shape(ic_einstein$end_points) +
tm_dots(col = "red") +
tm_shape(ic_einstein$start_point) +
tm_dots(col = "green")
```
Column {data-width=300}
-------------------------------------
### area size of the hull enclosing the routed points
```{r}
##############################################################
#
# PLOT
#
##############################################################
tm_shape(shape_districts_new) +
tm_polygons(alpha = 0,
popup.vars = c("area" = "AREA")) +
tm_shape(stops_area_berlin) +
tm_dots(col = "ic_area",
id = "name",
popup.vars = c("area" = "ic_area"),
size = 0.07,
border.lwd = 0.3,
legend.hist = TRUE,
n = 15,
title = "isochrone area [km^2]") +
tm_view(bbox = shape_berlin)
```
traveltimes to center {data-icon="fa-stopwatch"}
=====================================
```{r, include = FALSE}
##############################################################
#
# SHAPE CENTER AREAS
#
##############################################################
# "Zentrentragender Stadtraum mit höchster / hoher Urbanität"
# of Zentrumsbereichskernen
# see page 39: https://www.stadtentwicklung.berlin.de/planen/stadtentwicklungsplanung/download/zentren/2011-07-31_StEP_Zentren3.pdf
# or page 45 (less detailed): https://www.stadtentwicklung.berlin.de/planen/stadtentwicklungsplanung/download/zentren/StEP_Zentren_2030.pdf
# recreated with QGis
shape_center <- read_sf(dsn = "shape_center_areas", layer = "center_areas") %>%
mutate(name = c("east", "west")) %>%
select(name)
shape_center_east <- shape_center %>% filter(name == "east")
shape_center_west <- shape_center %>% filter(name == "west")
##############################################################
#
# READ GTFS DATA
#
##############################################################
# now we work with tidytransit
# calculation of shortest tt from all station to specific ones is more convinent
setwd_gtfs()
gtfs <- read_gtfs("2020-12_2020-12-28.zip")
setwd_work()
# http://tidytransit.r-transit.org/reference/filter_stop_times.html
stop_times_filtered <- filter_stop_times(gtfs, "2021-01-18", "06:00:00", "07:55:00")
##############################################################
#
# GET STOPS
#
##############################################################
stops <- st_as_sf(gtfs$stops, coords = c("stop_lon", "stop_lat"), crs = 4326) %>%
st_transform(25833) %>%
select(stop_name) %>%
rename(name = stop_name) %>%
distinct(name)
stops_berlin <- stops %>%
mutate(inside_berlin = st_within( geometry, shape_berlin )) %>%
mutate(inside_berlin = !is.na( as.numeric( inside_berlin ))) %>%
filter(inside_berlin == TRUE) %>%
select(name)
stops_center <- stops %>%
mutate(inside_center = st_within( geometry, shape_center )) %>%
mutate(inside_center = !is.na( as.numeric( inside_center ))) %>%
filter(inside_center == TRUE) %>%
select(name)
stops_center_east <- stops %>%
mutate(inside_center_east = st_within( geometry, shape_center_east )) %>%
mutate(inside_center_east = !is.na( as.numeric( inside_center_east ))) %>%
filter(inside_center_east == TRUE) %>%
select(name)
stops_center_west <- stops %>%
mutate(inside_center_west = st_within( geometry, shape_center_west )) %>%
mutate(inside_center_west = !is.na( as.numeric( inside_center_west ))) %>%
filter(inside_center_west == TRUE) %>%
select(name)
##############################################################
#
# TT calculation
#
##############################################################
# what are the tt to the center areas?
# according to Nahverkehrsplan Berlin 2019-2023: ANlage 1 - Monitoringbericht (p. 12)
# standard: tt_max = 3600, n_transfer_max = 2, n_realise_stations = 0.95
tt <- travel_times(
stop_times_filtered,
stops_center$name,
time_range = 5400,
arrival = TRUE,
max_transfers = 2,
# max_departure_time = NULL,
return_coords = TRUE,
return_DT = FALSE
)
# clean it for plot
tt <- tt %>%
rename(from = from_stop_name,
to = to_stop_name,
tt = travel_time,
departure = journey_departure_time,
arrival = journey_arrival_time
) %>%
select(-c(from_stop_id, to_stop_id, to_stop_lat, to_stop_lon)) %>%
st_as_sf(coords = c("from_stop_lon", "from_stop_lat"),
crs = 4326) %>%
st_transform(25833) %>%
mutate(tt = set_units(round(tt/60, 2), "min"))
```
Column {data-width=100}
-------------------------------------
### approach {data-height=400}
The [local transport plan](https://www.berlin.de/sen/uvk/verkehr/verkehrsplanung/oeffentlicher-personennahverkehr/nahverkehrsplan/) (p. 106) sets targets for the connectivity standards. Different categories of center areas (see [StEP](https://stadtentwicklung.berlin.de/planen/stadtentwicklungsplanung/de/zentren/zentren2030/index.shtml), p. 45) have to be reachable within a certain time and with a maximum number of transfer. This should hold for 95% of the stations.
Based on the GTFS data, we try to recreate the result of the monitoring ([NVP Anlage 1](https://www.berlin.de/sen/uvk/_assets/verkehr/verkehrsplanung/oeffentlicher-personennahverkehr/nahverkehrsplan/broschure_nvp_2019_anlage_1.pdf), p. 12), mentioning a degree of fulfillment of 99,7% for the central areas.
* destination:
+ City West (Zoo/ Kurfürstendamm)
+ Mitte (Potsdamer Platz/ Alexanderplatz)
* max. tt: 60min
* max. transfers: 2
From a more detailed [display of the area](https://www.stadtentwicklung.berlin.de/planen/stadtentwicklungsplanung/download/zentren/2011-07-31_StEP_Zentren3.pdf) (p. 39), we create a shape file enclosing the associated stations. The [tidytransit](https://tidytransit.r-transit.org/) package let's us [calculate](https://tidytransit.r-transit.org/reference/travel_times.html) the shortest travel time for all stations to any of a specified set of stations. For that `arrival` has to be set to `TRUE`.
We try to simulate a home–work trip in the morning rush hour arriving at 08:00. Assuming five minute walking at beginning and end of the trip, we limit the GTFS data to Monday (2021-01-18) and calculate the shortest travel times in a 90min time range ending at 07:55.
### Percent of all berlin stations fullfill the connectivity standard according to the GTFS data {data-height=200}
```{r}
##############################################################
#
# DEGREE OF FULLFILMENT
#
##############################################################
n_of_stations <- tt %>%
mutate(inside_berlin = st_within( geometry, shape_berlin )) %>%
mutate(inside_berlin = !is.na( as.numeric( inside_berlin ))) %>%
filter(inside_berlin == TRUE) %>%
mutate(outside_center = st_within( geometry, shape_center )) %>%
mutate(outside_center = is.na( as.numeric( outside_center ))) %>%
filter(outside_center == TRUE) %>%
nrow()
n_of_stations_valid <- tt %>%
mutate(inside_berlin = st_within( geometry, shape_berlin )) %>%
mutate(inside_berlin = !is.na( as.numeric( inside_berlin ))) %>%
filter(inside_berlin == TRUE) %>%
mutate(outside_center = st_within( geometry, shape_center )) %>%
mutate(outside_center = is.na( as.numeric( outside_center ))) %>%
filter(outside_center == TRUE) %>%
filter(tt <= 60 * as_units("min")) %>%
filter(transfers <= 2) %>%
nrow()
percent_stations_valid <- n_of_stations_valid / n_of_stations * 100
percent_stations_valid <- round(percent_stations_valid, 2)
valueBox(paste(percent_stations_valid, "%"), icon = "fa-crosshairs")
```
Column {data-width=300}
-------------------------------------
### shortest travel time to one of the stations inside City West or Mitte
```{r}
##############################################################
#
# PLOT
#
##############################################################
# https://campus.datacamp.com/courses/visualizing-geospatial-data-in-r/raster-data-and-color?ex=9
rdylgn <- rev(brewer.pal(7, "RdYlGn"))
# https://leaflet-extras.github.io/leaflet-providers/preview/
# https://tlorusso.github.io/geodata_workshop/tmap_package
# https://www.rdocumentation.org/packages/tmap/versions/3.0/topics/tm_basemap
# https://rdrr.io/cran/tmap/man/tm_view.html
# https://leafletjs.com/reference-1.3.4.html#map-methods-for-modifying-map-state
tm_basemap(leaflet::providers$CartoDB.DarkMatter) +
tm_shape(shape_districts_new) +
tm_polygons(alpha = 0,
lwd = 1.5,
border.col = "white",
popup.vars = c("area" = "AREA")
) +
tm_shape(shape_center) +
tm_polygons(alpha = 0.2,
col = "red",
border.col = "red"
) +
tm_shape(tt) +
tm_dots(col = "tt",
style = "fixed",
breaks = c(0, 10, 20, 30, 40, 50, 60, 120),
labels = c("0 – 10", "10 – 20", "20 – 30", "30 – 40", "40 – 50", "50 – 60", "> 60"),
id = "from",
palette = rdylgn,
title = "traveltime [min]",
popup.vars = c("to" = "to",
"traveltime" = "tt",
"departure at" = "departure",
"arrival at" = "arrival",
"number of transfers" = "transfers")
) +
tm_view(bbox = shape_center)
```
***
https://rstudio.github.io/leaflet/
- Interactive panning/zooming
- Compose maps using arbitrary combinations of map tiles, markers, polygons, lines, popups, and GeoJSON.
- Create maps right from the R console or RStudio
- Embed maps in knitr/R Markdown documents and Shiny apps
- Easily render Spatial objects from the sp package, or data frames with latitude/longitude columns
- Use map bounds and mouse events to drive Shiny logic